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This article introduces a new multiuser detection scheme which uses evolutionary programming (EP) to detect the user bits based on the maximum-likelihood decision rule. The major advantage of the proposed detector is that it has a lower computational complexity compared to other popular evolutionary-algorithm-based detectors. The simulation results show that the EP has always converged to the optimum solution with a small number of generations. The simulated average computational time performance demonstrates that this approach achieves practical ML performance with polynomial complexity in the number of users.